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Information × Registration Number 2119U006528, Article popup.category Препринт Title Medical image segmentation using shape prior information and deep neural networks (AI translated) popup.author Petryshak BohdanPetryshak Bohdan popup.publication 01-01-2019 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/4485 popup.publisher Description Semantic image segmentation is the task of classifying each pixel of an image into a corresponding category of what is being represented. It is an essential step to- wards automating image analysis process. However, the low-quality signal, high level of noise, variety of objects appearance, little amount of labeled data are the critical obstacles which stand on the way of achieving the perfect segmentation re- sults. Incorporating the shape prior knowledge has proven significant improvement of the segmentation results. In this work, we extend the existing method of incorpo- rating shape priors within the FCN segmentation framework to a multiclass seman- tic segmentation. We demonstrate the superiority of our extension in five different datasets and show that it capable of making the segmentation results more accurate and plausible in multiclass problems. . . . popup.nrat_date 2025-11-05 Close